MapReduce

Hadoop: Example process to generating a SequenceFile with image binaries to be processed in map/reduce

由 独自空忆成欢 提交于 2019-12-18 18:23:32
问题 Following Hadoop: how to access (many) photo images to be processed by map/reduce? question, where orangeoctopus provides a reasonable direction to load the image binaries and collect them into SequenceFiles to feed the mapper. Think this could be useful for the others also, as a local java process or probably a hadoop job in case of vast amount image files, I created this separate question to look for the example for the implementation. Thanks! 回答1: Hadoop: The Definitive Guide - Chapter 4

Hadoop: Example process to generating a SequenceFile with image binaries to be processed in map/reduce

由 不打扰是莪最后的温柔 提交于 2019-12-18 18:22:10
问题 Following Hadoop: how to access (many) photo images to be processed by map/reduce? question, where orangeoctopus provides a reasonable direction to load the image binaries and collect them into SequenceFiles to feed the mapper. Think this could be useful for the others also, as a local java process or probably a hadoop job in case of vast amount image files, I created this separate question to look for the example for the implementation. Thanks! 回答1: Hadoop: The Definitive Guide - Chapter 4

Hadoop mapreduce streaming from HBase

由 自作多情 提交于 2019-12-18 16:55:39
问题 I'm building a Hadoop (0.20.1) mapreduce job that uses HBase (0.20.1) as both the data source and data sink. I would like to write the job in Python which has required me to use hadoop-0.20.1-streaming.jar to stream data to and from my Python scripts. This works fine if the data source/sink are HDFS files. Does Hadoop support streaming from/to HBase for mapreduce? 回答1: This seems to do what I want but it's not part of the Hadoop distribution. Any other suggestions or comments still welcome.

Hadoop mapreduce streaming from HBase

由 痴心易碎 提交于 2019-12-18 16:54:40
问题 I'm building a Hadoop (0.20.1) mapreduce job that uses HBase (0.20.1) as both the data source and data sink. I would like to write the job in Python which has required me to use hadoop-0.20.1-streaming.jar to stream data to and from my Python scripts. This works fine if the data source/sink are HDFS files. Does Hadoop support streaming from/to HBase for mapreduce? 回答1: This seems to do what I want but it's not part of the Hadoop distribution. Any other suggestions or comments still welcome.

out of memory error when reading csv file in chunk

由 孤人 提交于 2019-12-18 16:32:24
问题 I am processing a csv -file which is 2.5 GB big. The 2.5 GB table looks like this: columns=[ka,kb_1,kb_2,timeofEvent,timeInterval] 0:'3M' '2345' '2345' '2014-10-5',3000 1:'3M' '2958' '2152' '2015-3-22',5000 2:'GE' '2183' '2183' '2012-12-31',515 3:'3M' '2958' '2958' '2015-3-10',395 4:'GE' '2183' '2285' '2015-4-19',1925 5:'GE' '2598' '2598' '2015-3-17',1915 And I want to groupby ka and kb_1 to get the result like this: columns=[ka,kb,errorNum,errorRate,totalNum of records] '3M','2345',0,0%,1

YARN的架构及原理

由 ￣綄美尐妖づ 提交于 2019-12-18 15:12:41
1. YARN产生背景 MapReduce本身存在着一些问题： 1）JobTracker单点故障问题；如果Hadoop集群的JobTracker挂掉，则整个分布式集群都不能使用了。 2）JobTracker承受的访问压力大，影响系统的扩展性。 3）不支持MapReduce之外的计算框架，比如Storm、Spark、Flink等。 与旧MapReduce相比，YARN采用了一种分层的集群框架，具有以下几种优势。 1）Hadoop2.0提出了HDFSFederation；它让多个NameNode分管不同的目录进而实现访问隔离和横向扩展。对于运行中NameNode的单点故障，通过 NameNode热备方案（NameNode HA）实现 。 2） YARN通过将资源管理和应用程序管理两部分剥离开来，分别由ResourceManager和ApplicationMaster进程来实现。其中，ResouceManager专管资源管理和调度，而ApplicationMaster则负责与具体应用程序相关的任务切分、任务调度和容错等。 3）YARN具有向后兼容性，用户在MR1上运行的作业，无需任何修改即可运行在YARN之上。 4）对于资源的表示以内存为单位（在目前版本的 Yarn 中没有考虑 CPU的占用），比之前以剩余 slot 数目为单位更合理。 5）支持多个框架，YARN不再是一个单纯的计算框架

combiner and reducer can be different?

由 廉价感情. 提交于 2019-12-18 15:09:19
问题 In many MapReduce programs, I see a reducer being used as a combiner as well. I know this is because of the specific nature of those programs. But I am wondering if they can be different. 回答1: Yes, a combiner can be different to the Reducer, although your Combiner will still be implementing the Reducer interface. Combiners can only be used in specific cases which are going to be job dependent. The Combiner will operate like a Reducer, but only on the subset of the Key/Values output from each

what is the basic difference between jobconf and job?

由 元气小坏坏 提交于 2019-12-18 14:15:23
问题 hi i wanted to know the basic difference between jobconf and job objects,currently i am submitting my job like this JobClient.runJob(jobconf); i saw other way of submitting jobs like this Configuration conf = getConf(); Job job = new Job(conf, "secondary sort"); job.waitForCompletion(true); return 0; and how can i specify the sortcomparator class for the job using jobconf? can any one explain me this concept? 回答1: In short: JobConf and everything else in the org.apache.hadoop.mapred package

第四章之Hadoop I/O

由 狂风中的少年 提交于 2019-12-18 14:10:11
数据的完整性 检测数据是否损坏的常见措施是：在数据第一次引入系统的时候计算校验和（checksum），并在数据通过一个不可靠的通道进行传输时候再次计算校验和，这样就能发现数据是否损坏。如果新的校验和和原来的校验和不匹配，我们就认为数据已经损坏。常用的数据检测码是:CRC-32(循环冗余校验) HDFS的数据完整性 datanode负责验证收到的数据后存储数据及其校验和，它在收到客户端的数据或复制期间其他datanode的数据时候执行这个操作。正在写数据的客户端将数据极其校验和发送到由一些列datanode组成的管线，管线中的最后一个datanode负责验证校验和。如果datanode检测到错误，客户端变收到一个ChecksumException异常。 客户端从datanode读取数据的时候，也会验证校验和，将他们与datanode中存储的校验和进行比较。每个datanode均持久保存有一个用户验证的校验和日志(persistent log of checksum verification)，so他知道每个数据块最后一次的验证时间。客户端成功验证一个数据块以后，会告诉这个datanode，datanode由此更新日志。不只是客户端在读取数据的时候会验证校验和，每个datanode也会在一个后台线程中运行DataBlockScanner

Is it possible to have multiple inputs with multiple different mappers in Hadoop MapReduce?

由 余生长醉 提交于 2019-12-18 13:10:07
问题 Is it possible to have multiple inputs with multiple different mappers in Hadoop MapReduce? Each mapper class work on a different set of inputs, but they would all emit key-value pairs consumed by the same reducer. Note that I'm not talking about chaining mappers here, I'm talking about running different mappers in parallel, not sequentially. 回答1: This is called a join. You want to use the mappers and reducers in the mapred.* packages (older, but still supported). The newer packages
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